Summary of A Survey on Large Language Models From Concept to Implementation, by Chen Wang et al.
A Survey on Large Language Models from Concept to Implementation
by Chen Wang, Jin Zhao, Jiaqi Gong
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures. It investigates the multifaceted applications of these models, with an emphasis on the GPT series. The paper highlights the transformative impact of AI-driven tools in revolutionizing traditional tasks like coding and problem-solving, while also paving new paths in research and development across diverse industries. Transformer models are used for code interpretation, image captioning, facilitating interactive systems, and advancing computational domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer models are Large Language Models that have been built using the Transformer architecture. These models can be used to help with tasks like coding and problem-solving. They can also be used to create new tools and technologies that can be used in different industries. The GPT series is a type of Transformer model that has been very successful. |
Keywords
» Artificial intelligence » Gpt » Image captioning » Transformer
